In [1]:
# Import libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
#import tensorflow as tf
#from tensorflow.examples.tutorials.mnist import input_data
%matplotlib inline
#import os
import tensorflow as tf
In [2]:
print(plt.get_backend())
module://ipykernel.pylab.backend_inline
In [3]:
homeb = pd.read_csv("../nilm_metadata/Smart_microgrid/House wise data/HomeB/2016/HomeB-meter1_2016.csv", index_col=0)
In [4]:
homeb_shape=homeb.shape
homeb_type=type(homeb)
print ("shape : "+str(homeb_shape) +" and type : " + str(homeb_type))
print (homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","Washer [kW]"].describe())
shape : (247600, 17) and type : <class 'pandas.core.frame.DataFrame'> count 48.000000 mean 0.008688 std 0.021093 min 0.003182 25% 0.003241 50% 0.003357 75% 0.003832 max 0.127577 Name: Washer [kW], dtype: float64
In [5]:
homeb.info
Out[5]:
<bound method DataFrame.info of use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-01-01 00:00:00 0.455814 0.0 0.455814 0.001166 0.189853
2016-01-01 00:30:00 0.403376 0.0 0.403376 0.000659 0.104036
2016-01-01 01:00:00 0.662962 0.0 0.662962 0.000823 0.107150
2016-01-01 01:30:00 0.677851 0.0 0.677851 0.001426 0.196811
2016-01-01 02:00:00 0.425556 0.0 0.425556 0.000759 0.119401
2016-01-01 02:30:00 0.448744 0.0 0.448744 0.000652 0.124341
2016-01-01 03:00:00 0.751886 0.0 0.751886 0.001643 0.325440
2016-01-01 03:30:00 0.554862 0.0 0.554862 0.001001 0.195691
2016-01-01 04:00:00 0.481422 0.0 0.481422 0.000929 0.183667
2016-01-01 04:30:00 0.640826 0.0 0.640826 0.002342 0.434424
2016-01-01 05:00:00 0.491982 0.0 0.491982 0.001082 0.211639
2016-01-01 05:30:00 0.450740 0.0 0.450740 0.000891 0.173173
2016-01-01 06:00:00 0.390099 0.0 0.390099 0.001082 0.202955
2016-01-01 06:30:00 0.477034 0.0 0.477034 0.001015 0.196979
2016-01-01 07:00:00 0.464292 0.0 0.464292 0.001019 0.195085
2016-01-01 07:30:00 0.421597 0.0 0.421597 0.001191 0.210581
2016-01-01 08:00:00 0.520880 0.0 0.520880 0.001125 0.206050
2016-01-01 08:30:00 0.280367 0.0 0.280367 0.000067 0.010183
2016-01-01 09:00:00 0.280385 0.0 0.280385 0.000074 0.009666
2016-01-01 09:30:00 0.397311 0.0 0.397311 0.000066 0.009701
2016-01-01 10:00:00 0.453370 0.0 0.453370 0.000155 0.009593
2016-01-01 10:30:00 0.432927 0.0 0.432927 0.000079 0.009718
2016-01-01 11:00:00 0.332945 0.0 0.332945 0.000036 0.009689
2016-01-01 11:30:00 0.197093 0.0 0.197093 0.000058 0.009607
2016-01-01 12:00:00 0.212063 0.0 0.212063 0.000057 0.009541
2016-01-01 12:30:00 0.305679 0.0 0.305679 0.000038 0.009622
2016-01-01 13:00:00 0.193844 0.0 0.193844 0.000061 0.009536
2016-01-01 13:30:00 0.201886 0.0 0.201886 0.000058 0.009632
2016-01-01 14:00:00 0.579818 0.0 0.579818 0.000842 0.009392
2016-01-01 14:30:00 1.846798 0.0 1.846798 0.007136 0.202987
... ... ... ... ... ...
2016-12-31 23:30:00 0.280300 0.0 0.280300 0.000300 0.008817
2016-12-31 23:31:00 0.279600 0.0 0.279600 0.000283 0.008800
2016-12-31 23:32:00 0.279767 0.0 0.279767 0.000283 0.008817
2016-12-31 23:33:00 0.280400 0.0 0.280400 0.000300 0.008817
2016-12-31 23:34:00 0.280267 0.0 0.280267 0.000283 0.008817
2016-12-31 23:35:00 0.294933 0.0 0.294933 0.000367 0.023333
2016-12-31 23:36:00 0.280567 0.0 0.280567 0.000300 0.008817
2016-12-31 23:37:00 0.280233 0.0 0.280233 0.000300 0.008817
2016-12-31 23:38:00 0.280700 0.0 0.280700 0.000283 0.008817
2016-12-31 23:39:00 0.280400 0.0 0.280400 0.000283 0.008833
2016-12-31 23:40:00 0.280400 0.0 0.280400 0.000300 0.008800
2016-12-31 23:41:00 0.279817 0.0 0.279817 0.000300 0.008817
2016-12-31 23:42:00 0.280150 0.0 0.280150 0.000283 0.008833
2016-12-31 23:43:00 0.280800 0.0 0.280800 0.000300 0.008867
2016-12-31 23:44:00 0.280633 0.0 0.280633 0.000300 0.008833
2016-12-31 23:45:00 0.281833 0.0 0.281833 0.000283 0.008900
2016-12-31 23:46:00 0.281267 0.0 0.281267 0.000300 0.008917
2016-12-31 23:47:00 0.280867 0.0 0.280867 0.000300 0.008900
2016-12-31 23:48:00 0.281117 0.0 0.281117 0.000283 0.008900
2016-12-31 23:49:00 0.329833 0.0 0.329833 0.000283 0.008917
2016-12-31 23:50:00 0.424533 0.0 0.424533 0.000267 0.008900
2016-12-31 23:51:00 0.409067 0.0 0.409067 0.000267 0.008933
2016-12-31 23:52:00 0.408533 0.0 0.408533 0.000267 0.008917
2016-12-31 23:53:00 0.407117 0.0 0.407117 0.000267 0.008917
2016-12-31 23:54:00 0.407150 0.0 0.407150 0.000267 0.008933
2016-12-31 23:55:00 0.406400 0.0 0.406400 0.000267 0.008950
2016-12-31 23:56:00 0.405350 0.0 0.405350 0.000267 0.008950
2016-12-31 23:57:00 0.404333 0.0 0.404333 0.000267 0.008900
2016-12-31 23:58:00 0.403883 0.0 0.403883 0.000267 0.008900
2016-12-31 23:59:00 0.382550 0.0 0.382550 0.000200 0.008883
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-01-01 00:00:00 0.006457 0.003416 0.032004
2016-01-01 00:30:00 0.006415 0.003429 0.031977
2016-01-01 01:00:00 0.006254 0.003527 0.119877
2016-01-01 01:30:00 0.006257 0.003562 0.132939
2016-01-01 02:00:00 0.006401 0.003441 0.054034
2016-01-01 02:30:00 0.006592 0.003307 0.003767
2016-01-01 03:00:00 0.006718 0.003231 0.003629
2016-01-01 03:30:00 0.006653 0.003261 0.003701
2016-01-01 04:00:00 0.006651 0.003241 0.003699
2016-01-01 04:30:00 0.021909 0.003182 0.003640
2016-01-01 05:00:00 0.006612 0.003232 0.003672
2016-01-01 05:30:00 0.006599 0.003240 0.003694
2016-01-01 06:00:00 0.006612 0.003255 0.003724
2016-01-01 06:30:00 0.006719 0.003241 0.003690
2016-01-01 07:00:00 0.006712 0.003234 0.013461
2016-01-01 07:30:00 0.006651 0.003297 0.031757
2016-01-01 08:00:00 0.006836 0.003274 0.031877
2016-01-01 08:30:00 0.006673 0.003328 0.031785
2016-01-01 09:00:00 0.006619 0.003317 0.031693
2016-01-01 09:30:00 0.015209 0.003297 0.031712
2016-01-01 10:00:00 0.059444 0.003314 0.031705
2016-01-01 10:30:00 0.030112 0.003232 0.022193
2016-01-01 11:00:00 0.006735 0.003198 0.003744
2016-01-01 11:30:00 0.006667 0.003224 0.003782
2016-01-01 12:00:00 0.006624 0.003207 0.003752
2016-01-01 12:30:00 0.006697 0.003197 0.003714
2016-01-01 13:00:00 0.006616 0.003229 0.003737
2016-01-01 13:30:00 0.006681 0.003225 0.003762
2016-01-01 14:00:00 0.006339 0.003814 0.003987
2016-01-01 14:30:00 0.004072 0.007419 0.005669
... ... ... ...
2016-12-31 23:30:00 0.005800 0.003450 0.051433
2016-12-31 23:31:00 0.005783 0.003467 0.051450
2016-12-31 23:32:00 0.005817 0.003467 0.051483
2016-12-31 23:33:00 0.005800 0.003450 0.051450
2016-12-31 23:34:00 0.005817 0.003467 0.051467
2016-12-31 23:35:00 0.005817 0.003450 0.051517
2016-12-31 23:36:00 0.005817 0.003467 0.051533
2016-12-31 23:37:00 0.005817 0.003450 0.051500
2016-12-31 23:38:00 0.005833 0.003450 0.051517
2016-12-31 23:39:00 0.005817 0.003450 0.051533
2016-12-31 23:40:00 0.005817 0.003450 0.051450
2016-12-31 23:41:00 0.005800 0.003450 0.051483
2016-12-31 23:42:00 0.005817 0.003467 0.051533
2016-12-31 23:43:00 0.005833 0.003450 0.051550
2016-12-31 23:44:00 0.005817 0.003467 0.051500
2016-12-31 23:45:00 0.005817 0.003467 0.051500
2016-12-31 23:46:00 0.005817 0.003467 0.051533
2016-12-31 23:47:00 0.005833 0.003450 0.051517
2016-12-31 23:48:00 0.005817 0.003467 0.051533
2016-12-31 23:49:00 0.005850 0.003450 0.051533
2016-12-31 23:50:00 0.005883 0.003433 0.051483
2016-12-31 23:51:00 0.005867 0.003433 0.051500
2016-12-31 23:52:00 0.005867 0.003433 0.051483
2016-12-31 23:53:00 0.005883 0.003433 0.051450
2016-12-31 23:54:00 0.005883 0.003433 0.051517
2016-12-31 23:55:00 0.005900 0.003450 0.051567
2016-12-31 23:56:00 0.005883 0.003433 0.051533
2016-12-31 23:57:00 0.005900 0.003433 0.051517
2016-12-31 23:58:00 0.005900 0.003450 0.051517
2016-12-31 23:59:00 0.005917 0.003367 0.051400
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-01-01 00:00:00 0.002309 0.004976
2016-01-01 00:30:00 0.002378 0.004958
2016-01-01 01:00:00 0.002413 0.012929
2016-01-01 01:30:00 0.002367 0.004988
2016-01-01 02:00:00 0.002423 0.005025
2016-01-01 02:30:00 0.002330 0.004991
2016-01-01 03:00:00 0.002167 0.005024
2016-01-01 03:30:00 0.002261 0.005003
2016-01-01 04:00:00 0.002260 0.004975
2016-01-01 04:30:00 0.002108 0.005061
2016-01-01 05:00:00 0.002238 0.004973
2016-01-01 05:30:00 0.002237 0.004929
2016-01-01 06:00:00 0.002234 0.004961
2016-01-01 06:30:00 0.002256 0.005004
2016-01-01 07:00:00 0.002256 0.004957
2016-01-01 07:30:00 0.002279 0.004996
2016-01-01 08:00:00 0.002298 0.005044
2016-01-01 08:30:00 0.002438 0.004976
2016-01-01 09:00:00 0.002424 0.004939
2016-01-01 09:30:00 0.002433 0.004939
2016-01-01 10:00:00 0.002535 0.004947
2016-01-01 10:30:00 0.002444 0.018252
2016-01-01 11:00:00 0.002355 0.004806
2016-01-01 11:30:00 0.002377 0.004812
2016-01-01 12:00:00 0.002354 0.004758
2016-01-01 12:30:00 0.002362 0.004798
2016-01-01 13:00:00 0.002402 0.004859
2016-01-01 13:30:00 0.002399 0.004854
2016-01-01 14:00:00 0.002533 0.012481
2016-01-01 14:30:00 0.003334 0.005259
... ... ...
2016-12-31 23:30:00 0.003333 0.004883
2016-12-31 23:31:00 0.003333 0.004917
2016-12-31 23:32:00 0.003350 0.004933
2016-12-31 23:33:00 0.003333 0.004917
2016-12-31 23:34:00 0.003350 0.004917
2016-12-31 23:35:00 0.003333 0.004900
2016-12-31 23:36:00 0.003350 0.004917
2016-12-31 23:37:00 0.003333 0.004917
2016-12-31 23:38:00 0.003333 0.004883
2016-12-31 23:39:00 0.003333 0.004917
2016-12-31 23:40:00 0.003333 0.004900
2016-12-31 23:41:00 0.003333 0.004883
2016-12-31 23:42:00 0.003317 0.004900
2016-12-31 23:43:00 0.003333 0.004900
2016-12-31 23:44:00 0.003333 0.004900
2016-12-31 23:45:00 0.003333 0.004900
2016-12-31 23:46:00 0.003350 0.004917
2016-12-31 23:47:00 0.003333 0.004933
2016-12-31 23:48:00 0.003350 0.004933
2016-12-31 23:49:00 0.003350 0.004950
2016-12-31 23:50:00 0.003350 0.004933
2016-12-31 23:51:00 0.003333 0.004967
2016-12-31 23:52:00 0.003350 0.004967
2016-12-31 23:53:00 0.003333 0.004933
2016-12-31 23:54:00 0.003333 0.004950
2016-12-31 23:55:00 0.003333 0.004950
2016-12-31 23:56:00 0.003350 0.004967
2016-12-31 23:57:00 0.003350 0.004967
2016-12-31 23:58:00 0.003350 0.004967
2016-12-31 23:59:00 0.003333 0.004967
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-01-01 00:00:00 0.090664 0.000021
2016-01-01 00:30:00 0.090591 0.000068
2016-01-01 01:00:00 0.090467 0.000173
2016-01-01 01:30:00 0.090688 0.000123
2016-01-01 02:00:00 0.077748 0.000040
2016-01-01 02:30:00 0.068908 0.000068
2016-01-01 03:00:00 0.068744 0.000064
2016-01-01 03:30:00 0.063393 0.000081
2016-01-01 04:00:00 0.056010 0.000059
2016-01-01 04:30:00 0.055681 0.000080
2016-01-01 05:00:00 0.055700 0.000038
2016-01-01 05:30:00 0.055679 0.000054
2016-01-01 06:00:00 0.055396 0.000012
2016-01-01 06:30:00 0.055600 0.000050
2016-01-01 07:00:00 0.055504 0.000039
2016-01-01 07:30:00 0.055432 0.000007
2016-01-01 08:00:00 0.055806 0.000056
2016-01-01 08:30:00 0.055760 0.000089
2016-01-01 09:00:00 0.055729 0.000062
2016-01-01 09:30:00 0.055844 0.000131
2016-01-01 10:00:00 0.063675 0.000129
2016-01-01 10:30:00 0.059654 0.000113
2016-01-01 11:00:00 0.055676 0.000128
2016-01-01 11:30:00 0.055546 0.000052
2016-01-01 12:00:00 0.055494 0.000064
2016-01-01 12:30:00 0.055548 0.000127
2016-01-01 13:00:00 0.055446 0.000048
2016-01-01 13:30:00 0.055501 0.000056
2016-01-01 14:00:00 0.278134 0.000270
2016-01-01 14:30:00 1.526282 0.000881
... ... ...
2016-12-31 23:30:00 0.089400 0.000100
2016-12-31 23:31:00 0.089467 0.000067
2016-12-31 23:32:00 0.089350 0.000133
2016-12-31 23:33:00 0.090067 0.000167
2016-12-31 23:34:00 0.090000 0.000067
2016-12-31 23:35:00 0.090050 0.000100
2016-12-31 23:36:00 0.090067 0.000100
2016-12-31 23:37:00 0.090067 0.000133
2016-12-31 23:38:00 0.090333 0.000133
2016-12-31 23:39:00 0.090067 0.000100
2016-12-31 23:40:00 0.090083 0.000100
2016-12-31 23:41:00 0.090017 0.000133
2016-12-31 23:42:00 0.090167 0.000067
2016-12-31 23:43:00 0.090583 0.000133
2016-12-31 23:44:00 0.090600 0.000100
2016-12-31 23:45:00 0.090917 0.000100
2016-12-31 23:46:00 0.090933 0.000100
2016-12-31 23:47:00 0.090733 0.000100
2016-12-31 23:48:00 0.090667 0.000067
2016-12-31 23:49:00 0.090617 0.000133
2016-12-31 23:50:00 0.090683 0.000200
2016-12-31 23:51:00 0.090617 0.000200
2016-12-31 23:52:00 0.090783 0.000200
2016-12-31 23:53:00 0.090750 0.000233
2016-12-31 23:54:00 0.090700 0.000233
2016-12-31 23:55:00 0.090567 0.000200
2016-12-31 23:56:00 0.090017 0.000200
2016-12-31 23:57:00 0.089817 0.000200
2016-12-31 23:58:00 0.089850 0.000200
2016-12-31 23:59:00 0.068817 0.000167
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-01-01 00:00:00 0.000347 0.039824
2016-01-01 00:30:00 0.000353 0.039098
2016-01-01 01:00:00 0.000429 0.038571
2016-01-01 01:30:00 0.000429 0.039092
2016-01-01 02:00:00 0.000340 0.039470
2016-01-01 02:30:00 0.000353 0.039535
2016-01-01 03:00:00 0.000434 0.039386
2016-01-01 03:30:00 0.000387 0.039337
2016-01-01 04:00:00 0.000363 0.039155
2016-01-01 04:30:00 0.000349 0.039653
2016-01-01 05:00:00 0.000356 0.038921
2016-01-01 05:30:00 0.000341 0.038644
2016-01-01 06:00:00 0.000299 0.038913
2016-01-01 06:30:00 0.000343 0.039547
2016-01-01 07:00:00 0.000334 0.039463
2016-01-01 07:30:00 0.000306 0.039394
2016-01-01 08:00:00 0.000361 0.040014
2016-01-01 08:30:00 0.000308 0.039691
2016-01-01 09:00:00 0.000287 0.039694
2016-01-01 09:30:00 0.000347 0.039563
2016-01-01 10:00:00 0.000348 0.039241
2016-01-01 10:30:00 0.000334 0.039175
2016-01-01 11:00:00 0.000337 0.039387
2016-01-01 11:30:00 0.000288 0.039584
2016-01-01 12:00:00 0.000296 0.039337
2016-01-01 12:30:00 0.000344 0.039386
2016-01-01 13:00:00 0.000301 0.039237
2016-01-01 13:30:00 0.000301 0.039454
2016-01-01 14:00:00 0.000509 0.038450
2016-01-01 14:30:00 0.001317 0.039967
... ... ...
2016-12-31 23:30:00 0.000367 0.003833
2016-12-31 23:31:00 0.000383 0.003850
2016-12-31 23:32:00 0.000367 0.003867
2016-12-31 23:33:00 0.000367 0.003850
2016-12-31 23:34:00 0.000383 0.003867
2016-12-31 23:35:00 0.000367 0.003867
2016-12-31 23:36:00 0.000383 0.003850
2016-12-31 23:37:00 0.000367 0.003867
2016-12-31 23:38:00 0.000367 0.003850
2016-12-31 23:39:00 0.000383 0.003867
2016-12-31 23:40:00 0.000367 0.003867
2016-12-31 23:41:00 0.000367 0.003867
2016-12-31 23:42:00 0.000383 0.003850
2016-12-31 23:43:00 0.000367 0.003867
2016-12-31 23:44:00 0.000367 0.003883
2016-12-31 23:45:00 0.000367 0.003867
2016-12-31 23:46:00 0.000383 0.003867
2016-12-31 23:47:00 0.000367 0.003867
2016-12-31 23:48:00 0.000367 0.003867
2016-12-31 23:49:00 0.000400 0.003817
2016-12-31 23:50:00 0.000450 0.003767
2016-12-31 23:51:00 0.000450 0.003767
2016-12-31 23:52:00 0.000433 0.003783
2016-12-31 23:53:00 0.000433 0.003783
2016-12-31 23:54:00 0.000450 0.003767
2016-12-31 23:55:00 0.000433 0.003800
2016-12-31 23:56:00 0.000433 0.003783
2016-12-31 23:57:00 0.000450 0.003783
2016-12-31 23:58:00 0.000433 0.003800
2016-12-31 23:59:00 0.000433 0.003767
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-01-01 00:00:00 0.000543 0.004767 0.007030
2016-01-01 00:30:00 0.000783 0.004856 0.043428
2016-01-01 01:00:00 0.000757 0.066279 0.134296
2016-01-01 01:30:00 0.000589 0.004738 0.091560
2016-01-01 02:00:00 0.000666 0.004804 0.004786
2016-01-01 02:30:00 0.000738 0.004861 0.092701
2016-01-01 03:00:00 0.000295 0.004696 0.209441
2016-01-01 03:30:00 0.000607 0.004814 0.143477
2016-01-01 04:00:00 0.000627 0.004823 0.117937
2016-01-01 04:30:00 0.000071 0.004592 0.004542
2016-01-01 05:00:00 0.000574 0.004769 0.101941
2016-01-01 05:30:00 0.000769 0.004864 0.101054
2016-01-01 06:00:00 0.000706 0.004843 0.004496
2016-01-01 06:30:00 0.000714 0.004927 0.103627
2016-01-01 07:00:00 0.000775 0.004897 0.084492
2016-01-01 07:30:00 0.000630 0.004870 0.004572
2016-01-01 08:00:00 0.000728 0.004962 0.114857
2016-01-01 08:30:00 0.001001 0.005088 0.065381
2016-01-01 09:00:00 0.001002 0.005067 0.004549
2016-01-01 09:30:00 0.001049 0.005122 0.117674
2016-01-01 10:00:00 0.001124 0.005151 0.046622
2016-01-01 10:30:00 0.001090 0.005120 0.046247
2016-01-01 11:00:00 0.001072 0.005053 0.119261
2016-01-01 11:30:00 0.001000 0.004980 0.004462
2016-01-01 12:00:00 0.001001 0.004949 0.023140
2016-01-01 12:30:00 0.001001 0.005013 0.125196
2016-01-01 13:00:00 0.000999 0.004976 0.004481
2016-01-01 13:30:00 0.000984 0.004975 0.012528
2016-01-01 14:00:00 0.000749 0.004768 0.131491
2016-01-01 14:30:00 0.000098 0.004186 0.006492
... ... ... ...
2016-12-31 23:30:00 0.046783 0.004850 0.003083
2016-12-31 23:31:00 0.046000 0.004833 0.003083
2016-12-31 23:32:00 0.046033 0.004817 0.003083
2016-12-31 23:33:00 0.046000 0.004833 0.003100
2016-12-31 23:34:00 0.045983 0.004833 0.003083
2016-12-31 23:35:00 0.045983 0.004817 0.003100
2016-12-31 23:36:00 0.046000 0.004817 0.003083
2016-12-31 23:37:00 0.045917 0.004833 0.003100
2016-12-31 23:38:00 0.046000 0.004833 0.003083
2016-12-31 23:39:00 0.045933 0.004817 0.003100
2016-12-31 23:40:00 0.045900 0.004833 0.003083
2016-12-31 23:41:00 0.045617 0.004817 0.003083
2016-12-31 23:42:00 0.045617 0.004833 0.003083
2016-12-31 23:43:00 0.045633 0.004833 0.003100
2016-12-31 23:44:00 0.045667 0.004833 0.003100
2016-12-31 23:45:00 0.045983 0.004833 0.003100
2016-12-31 23:46:00 0.045733 0.004850 0.003083
2016-12-31 23:47:00 0.045700 0.004850 0.003100
2016-12-31 23:48:00 0.045850 0.004850 0.003117
2016-12-31 23:49:00 0.045850 0.004850 0.051700
2016-12-31 23:50:00 0.046000 0.004933 0.145600
2016-12-31 23:51:00 0.045883 0.004917 0.130200
2016-12-31 23:52:00 0.045983 0.004917 0.129567
2016-12-31 23:53:00 0.045900 0.004917 0.128517
2016-12-31 23:54:00 0.045883 0.004917 0.128583
2016-12-31 23:55:00 0.045867 0.004900 0.127633
2016-12-31 23:56:00 0.045967 0.004900 0.127083
2016-12-31 23:57:00 0.045933 0.004917 0.126417
2016-12-31 23:58:00 0.046017 0.004900 0.125833
2016-12-31 23:59:00 0.046067 0.004917 0.125283
[247600 rows x 17 columns]>
In [6]:
print(homeb.head(10))
print(homeb.tail())
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-01-01 00:00:00 0.455814 0.0 0.455814 0.001166 0.189853
2016-01-01 00:30:00 0.403376 0.0 0.403376 0.000659 0.104036
2016-01-01 01:00:00 0.662962 0.0 0.662962 0.000823 0.107150
2016-01-01 01:30:00 0.677851 0.0 0.677851 0.001426 0.196811
2016-01-01 02:00:00 0.425556 0.0 0.425556 0.000759 0.119401
2016-01-01 02:30:00 0.448744 0.0 0.448744 0.000652 0.124341
2016-01-01 03:00:00 0.751886 0.0 0.751886 0.001643 0.325440
2016-01-01 03:30:00 0.554862 0.0 0.554862 0.001001 0.195691
2016-01-01 04:00:00 0.481422 0.0 0.481422 0.000929 0.183667
2016-01-01 04:30:00 0.640826 0.0 0.640826 0.002342 0.434424
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-01-01 00:00:00 0.006457 0.003416 0.032004
2016-01-01 00:30:00 0.006415 0.003429 0.031977
2016-01-01 01:00:00 0.006254 0.003527 0.119877
2016-01-01 01:30:00 0.006257 0.003562 0.132939
2016-01-01 02:00:00 0.006401 0.003441 0.054034
2016-01-01 02:30:00 0.006592 0.003307 0.003767
2016-01-01 03:00:00 0.006718 0.003231 0.003629
2016-01-01 03:30:00 0.006653 0.003261 0.003701
2016-01-01 04:00:00 0.006651 0.003241 0.003699
2016-01-01 04:30:00 0.021909 0.003182 0.003640
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-01-01 00:00:00 0.002309 0.004976
2016-01-01 00:30:00 0.002378 0.004958
2016-01-01 01:00:00 0.002413 0.012929
2016-01-01 01:30:00 0.002367 0.004988
2016-01-01 02:00:00 0.002423 0.005025
2016-01-01 02:30:00 0.002330 0.004991
2016-01-01 03:00:00 0.002167 0.005024
2016-01-01 03:30:00 0.002261 0.005003
2016-01-01 04:00:00 0.002260 0.004975
2016-01-01 04:30:00 0.002108 0.005061
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-01-01 00:00:00 0.090664 0.000021
2016-01-01 00:30:00 0.090591 0.000068
2016-01-01 01:00:00 0.090467 0.000173
2016-01-01 01:30:00 0.090688 0.000123
2016-01-01 02:00:00 0.077748 0.000040
2016-01-01 02:30:00 0.068908 0.000068
2016-01-01 03:00:00 0.068744 0.000064
2016-01-01 03:30:00 0.063393 0.000081
2016-01-01 04:00:00 0.056010 0.000059
2016-01-01 04:30:00 0.055681 0.000080
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-01-01 00:00:00 0.000347 0.039824
2016-01-01 00:30:00 0.000353 0.039098
2016-01-01 01:00:00 0.000429 0.038571
2016-01-01 01:30:00 0.000429 0.039092
2016-01-01 02:00:00 0.000340 0.039470
2016-01-01 02:30:00 0.000353 0.039535
2016-01-01 03:00:00 0.000434 0.039386
2016-01-01 03:30:00 0.000387 0.039337
2016-01-01 04:00:00 0.000363 0.039155
2016-01-01 04:30:00 0.000349 0.039653
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-01-01 00:00:00 0.000543 0.004767 0.007030
2016-01-01 00:30:00 0.000783 0.004856 0.043428
2016-01-01 01:00:00 0.000757 0.066279 0.134296
2016-01-01 01:30:00 0.000589 0.004738 0.091560
2016-01-01 02:00:00 0.000666 0.004804 0.004786
2016-01-01 02:30:00 0.000738 0.004861 0.092701
2016-01-01 03:00:00 0.000295 0.004696 0.209441
2016-01-01 03:30:00 0.000607 0.004814 0.143477
2016-01-01 04:00:00 0.000627 0.004823 0.117937
2016-01-01 04:30:00 0.000071 0.004592 0.004542
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
Date & Time
2016-12-31 23:55:00 0.406400 0.0 0.406400 0.000267 0.008950
2016-12-31 23:56:00 0.405350 0.0 0.405350 0.000267 0.008950
2016-12-31 23:57:00 0.404333 0.0 0.404333 0.000267 0.008900
2016-12-31 23:58:00 0.403883 0.0 0.403883 0.000267 0.008900
2016-12-31 23:59:00 0.382550 0.0 0.382550 0.000200 0.008883
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
Date & Time
2016-12-31 23:55:00 0.005900 0.003450 0.051567
2016-12-31 23:56:00 0.005883 0.003433 0.051533
2016-12-31 23:57:00 0.005900 0.003433 0.051517
2016-12-31 23:58:00 0.005900 0.003450 0.051517
2016-12-31 23:59:00 0.005917 0.003367 0.051400
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
Date & Time
2016-12-31 23:55:00 0.003333 0.004950
2016-12-31 23:56:00 0.003350 0.004967
2016-12-31 23:57:00 0.003350 0.004967
2016-12-31 23:58:00 0.003350 0.004967
2016-12-31 23:59:00 0.003333 0.004967
MBed + KBed outlets [kW] Dryer + egauge [kW] \
Date & Time
2016-12-31 23:55:00 0.090567 0.000200
2016-12-31 23:56:00 0.090017 0.000200
2016-12-31 23:57:00 0.089817 0.000200
2016-12-31 23:58:00 0.089850 0.000200
2016-12-31 23:59:00 0.068817 0.000167
Panel GFI (central vac) [kW] Home Office (R) [kW] \
Date & Time
2016-12-31 23:55:00 0.000433 0.003800
2016-12-31 23:56:00 0.000433 0.003783
2016-12-31 23:57:00 0.000450 0.003783
2016-12-31 23:58:00 0.000433 0.003800
2016-12-31 23:59:00 0.000433 0.003767
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
Date & Time
2016-12-31 23:55:00 0.045867 0.004900 0.127633
2016-12-31 23:56:00 0.045967 0.004900 0.127083
2016-12-31 23:57:00 0.045933 0.004917 0.126417
2016-12-31 23:58:00 0.046017 0.004900 0.125833
2016-12-31 23:59:00 0.046067 0.004917 0.125283
In [7]:
homeb_description=homeb.describe()
print (homeb_description)
use [kW] gen [kW] Grid [kW] AC [kW] Furnace [kW] \
count 247600.000000 247600.0 247600.000000 247600.000000 247600.000000
mean 1.035553 0.0 1.035553 0.342994 0.116331
std 1.017468 0.0 1.017468 0.868884 0.181299
min 0.004017 0.0 0.004017 0.000000 0.000003
25% 0.381183 0.0 0.381183 0.000100 0.009000
50% 0.670183 0.0 0.670183 0.000350 0.009350
75% 1.094150 0.0 1.094150 0.002483 0.260437
max 9.381150 0.0 9.381150 4.303854 0.586683
Cellar Lights [kW] Washer [kW] First Floor lights [kW] \
count 247600.000000 247600.000000 247600.000000
mean 0.008922 0.004404 0.031873
std 0.017014 0.015072 0.039393
min 0.000017 0.000000 0.000033
25% 0.005517 0.003083 0.003800
50% 0.005817 0.003233 0.030450
75% 0.005983 0.003350 0.031983
max 0.186483 0.465350 0.878296
Utility Rm + Basement Bath [kW] Garage outlets [kW] \
count 247600.000000 247600.000000
mean 0.160300 0.005410
std 0.250489 0.007141
min 0.000267 0.000017
25% 0.003267 0.004800
50% 0.003533 0.004900
75% 0.272192 0.004983
max 1.546676 0.697683
MBed + KBed outlets [kW] Dryer + egauge [kW] \
count 247600.000000 247600.000000
mean 0.082901 0.022582
std 0.122644 0.290628
min 0.000033 0.000000
25% 0.056850 0.000033
50% 0.062683 0.000067
75% 0.070833 0.000100
max 2.592283 6.054800
Panel GFI (central vac) [kW] Home Office (R) [kW] \
count 247600.000000 247600.000000
mean 0.000404 0.123603
std 0.007269 0.167647
min 0.000000 0.000000
25% 0.000217 0.003067
50% 0.000300 0.016644
75% 0.000367 0.339850
max 0.768017 0.500833
Dining room (R) [kW] Microwave (R) [kW] Fridge (R) [kW]
count 247600.000000 247600.000000 247600.000000
mean 0.040586 0.012301 0.072069
std 0.019152 0.102988 0.076897
min 0.000000 0.000067 0.000867
25% 0.032767 0.004433 0.004000
50% 0.033750 0.004683 0.045383
75% 0.045183 0.004883 0.133167
max 0.911288 1.923833 1.132350
In [8]:
print(len(homeb.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59","AC [kW]"]))
print(homeb.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59","AC [kW]"].shape)
print(homeb.loc["2016-01-03 00:00:00":"2016-01-03 23:59:59","AC [kW]"].shape)
48 (48,) (48,)
In [9]:
ac_data=homeb.loc[:,"AC [kW]"]
washer_data=homeb.loc[:,"Washer [kW]"]
grid_data=homeb.loc[:,"Grid [kW]"]
ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe() ac_data_daywise=homeb.loc["2016-12-06 00:00:00":"2016-12-06 23:59:59","AC [kW]"] ac_data_daywise.describe()
ac_data_daywise=homeb.loc["2016-09-03 00:00:00":"2016-09-03 23:59:59","AC [kW]"] ac_data_daywise.describe()
In [10]:
import datetime
start_date=datetime.date(2016,1,1)
end_date=datetime.date(2016,12,31)
inc_1_days=datetime.timedelta(days=1)
#print (start_date)
#print (start_date+inc_1_days)
ac_data_list=[]
while (start_date <= end_date):
#print (start_date)
#print(len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]))
#print(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max())
ac_data_list.append({"date":str(start_date),"no_of_data":len(homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"]),"Peak_Power":homeb.loc[str(start_date)+" 00:00:00":str(start_date)+"23:59:59","AC [kW]"].max()})
start_date=start_date+inc_1_days
#print (ac_data)
ac_data_day_wise=pd.DataFrame(ac_data_list)
ac_data_day_wise.describe(include='O')
ac_data_day_wise.describe()
ac_data_day_wise_1440=ac_data_day_wise[ac_data_day_wise["no_of_data"]==1440]
ac_data_day_wise.set_index("date",inplace=True)
ac_data_day_wise_1440.set_index("date",inplace=True)
pd.set_option('display.max_rows', 1000)
print(ac_data_day_wise["no_of_data"].value_counts())
print(ac_data_day_wise_1440.info)
48 199 1440 164 1500 1 342 1 46 1 Name: no_of_data, dtype: int64 <bound method DataFrame.info of Peak_Power no_of_data date 2016-07-20 2.541317 1440 2016-07-21 2.739450 1440 2016-07-22 2.868167 1440 2016-07-23 2.805167 1440 2016-07-24 2.760617 1440 2016-07-25 2.816733 1440 2016-07-26 2.761800 1440 2016-07-27 2.808667 1440 2016-07-28 2.773983 1440 2016-07-29 2.731933 1440 2016-07-30 2.664517 1440 2016-07-31 2.473683 1440 2016-08-01 2.520417 1440 2016-08-02 2.503650 1440 2016-08-03 2.646683 1440 2016-08-04 2.660650 1440 2016-08-05 2.730233 1440 2016-08-06 2.723617 1440 2016-08-07 2.690383 1440 2016-08-08 2.676767 1440 2016-08-09 2.722517 1440 2016-08-10 2.634617 1440 2016-08-11 2.809017 1440 2016-08-12 2.847450 1440 2016-08-13 2.828167 1440 2016-08-14 2.822833 1440 2016-08-15 2.723917 1440 2016-08-16 2.676733 1440 2016-08-17 2.624067 1440 2016-08-18 2.742367 1440 2016-08-19 2.702283 1440 2016-08-20 2.695400 1440 2016-08-21 2.652767 1440 2016-08-22 2.496350 1440 2016-08-23 2.635567 1440 2016-08-24 2.692117 1440 2016-08-25 2.697917 1440 2016-08-26 2.771733 1440 2016-08-27 2.731900 1440 2016-08-28 2.725483 1440 2016-08-29 2.706500 1440 2016-08-30 2.707133 1440 2016-08-31 2.603433 1440 2016-09-01 2.559233 1440 2016-09-02 2.572700 1440 2016-09-03 0.003183 1440 2016-09-04 0.003433 1440 2016-09-05 0.003267 1440 2016-09-06 2.485983 1440 2016-09-07 2.455917 1440 2016-09-08 2.762900 1440 2016-09-09 2.762267 1440 2016-09-10 2.688500 1440 2016-09-11 2.611383 1440 2016-09-12 0.003100 1440 2016-09-13 0.003383 1440 2016-09-14 0.003450 1440 2016-09-15 0.002967 1440 2016-09-16 0.001733 1440 2016-09-17 0.000750 1440 2016-09-18 2.594850 1440 2016-09-19 2.479600 1440 2016-09-20 2.616400 1440 2016-09-21 2.595167 1440 2016-09-22 2.693433 1440 2016-09-23 2.579983 1440 2016-09-24 0.002900 1440 2016-09-25 0.002517 1440 2016-09-26 0.000933 1440 2016-09-27 0.003017 1440 2016-09-28 0.003867 1440 2016-09-29 0.003467 1440 2016-09-30 0.003033 1440 2016-10-01 0.003917 1440 2016-10-02 0.002950 1440 2016-10-03 0.003417 1440 2016-10-04 0.003083 1440 2016-10-05 0.003467 1440 2016-10-06 0.003033 1440 2016-10-07 0.003200 1440 2016-10-08 0.002667 1440 2016-10-09 0.003083 1440 2016-10-10 0.003800 1440 2016-10-11 0.010300 1440 2016-10-12 0.006000 1440 2016-10-13 0.002850 1440 2016-10-14 0.003083 1440 2016-10-15 0.006183 1440 2016-10-16 0.000283 1440 2016-10-17 0.002733 1440 2016-10-18 0.002983 1440 2016-10-19 0.005633 1440 2016-10-20 0.002767 1440 2016-10-21 0.003217 1440 2016-10-22 0.002883 1440 2016-10-23 0.002550 1440 2016-10-24 0.003767 1440 2016-10-25 0.006300 1440 2016-10-26 0.002900 1440 2016-10-27 0.002950 1440 2016-10-28 0.002783 1440 2016-10-29 0.006450 1440 2016-10-30 0.006367 1440 2016-10-31 0.002817 1440 2016-11-01 0.008817 1440 2016-11-02 0.002483 1440 2016-11-03 0.002383 1440 2016-11-04 0.005617 1440 2016-11-05 0.005150 1440 2016-11-07 0.003067 1440 2016-11-08 0.003217 1440 2016-11-09 0.003517 1440 2016-11-10 0.002967 1440 2016-11-11 0.003033 1440 2016-11-12 0.003000 1440 2016-11-13 0.003000 1440 2016-11-14 0.003083 1440 2016-11-15 0.003100 1440 2016-11-16 0.002783 1440 2016-11-17 0.003117 1440 2016-11-18 0.006500 1440 2016-11-19 0.009517 1440 2016-11-20 0.008400 1440 2016-11-21 0.005567 1440 2016-11-22 0.009583 1440 2016-11-23 0.003567 1440 2016-11-24 0.003683 1440 2016-11-25 0.003017 1440 2016-11-26 0.006117 1440 2016-11-27 0.002917 1440 2016-11-28 0.003500 1440 2016-11-29 0.002983 1440 2016-11-30 0.002850 1440 2016-12-01 0.002967 1440 2016-12-02 0.003100 1440 2016-12-03 0.003000 1440 2016-12-04 0.003650 1440 2016-12-05 0.003417 1440 2016-12-06 0.004017 1440 2016-12-07 0.003050 1440 2016-12-08 0.003600 1440 2016-12-09 0.003167 1440 2016-12-10 0.003183 1440 2016-12-11 0.003567 1440 2016-12-12 0.003067 1440 2016-12-13 0.003350 1440 2016-12-14 0.003800 1440 2016-12-15 0.009500 1440 2016-12-16 0.003733 1440 2016-12-17 0.009033 1440 2016-12-18 0.003533 1440 2016-12-19 0.003533 1440 2016-12-20 0.009300 1440 2016-12-21 0.009250 1440 2016-12-22 0.010150 1440 2016-12-23 0.009300 1440 2016-12-24 0.003300 1440 2016-12-25 0.003133 1440 2016-12-26 0.003533 1440 2016-12-27 0.003600 1440 2016-12-28 0.003183 1440 2016-12-29 0.009717 1440 2016-12-30 0.003233 1440 2016-12-31 0.003283 1440>
In [11]:
plt.figure()
fig, axes = plt.subplots(nrows=2, ncols=2)
ac_data.loc["2016-01-01 00:00:00":"2016-01-01 23:59:59"].plot.line(ax=axes[0,0])
ac_data.loc["2016-01-02 00:00:00":"2016-01-02 23:59:59"].plot.line(style='g', ax=axes[0,0])
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0,1])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g',ax=axes[1,1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(style='g',ax=axes[1,0])
Out[11]:
<matplotlib.axes._subplots.AxesSubplot at 0x12aa3240>
<Figure size 432x288 with 0 Axes>
In [12]:
plt.figure(1)
fig, axes = plt.subplots(nrows=10, ncols=1)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=axes[0])
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(style='g', ax=axes[1])
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=axes[2])
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(style='g',ax=axes[3])
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(style='g',ax=axes[4])
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=axes[5])
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(style='g', ax=axes[6])
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=axes[7])
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(style='g',ax=axes[8])
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(style='g',ax=axes[9])
Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x12c50080>
<Figure size 432x288 with 0 Axes>
In [13]:
plt.figure(1)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-20 00:00:00":"2016-07-20 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-20")
ax[1].set_title("Grid Data 2016-07-20")
plt.figure(2)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-21 00:00:00":"2016-07-21 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-21")
ax[1].set_title("Grid Data 2016-07-21")
plt.figure(3)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-22 00:00:00":"2016-07-22 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-22")
ax[1].set_title("Grid Data 2016-07-22")
plt.figure(4)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-23 00:00:00":"2016-07-23 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-23")
ax[1].set_title("Grid Data 2016-07-23")
plt.figure(5)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-24 00:00:00":"2016-07-24 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-24")
ax[1].set_title("Grid Data 2016-07-24")
plt.figure(6)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-25 00:00:00":"2016-07-25 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-25")
ax[1].set_title("Grid Data 2016-07-25")
plt.figure(7)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-26 00:00:00":"2016-07-26 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-26")
ax[1].set_title("Grid Data 2016-07-26")
plt.figure(8)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-27 00:00:00":"2016-07-27 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-27")
ax[1].set_title("Grid Data 2016-07-27")
plt.figure(9)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-28 00:00:00":"2016-07-28 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data 2016-07-28")
ax[1].set_title("Grid Data 2016-07-28")
plt.figure(10)
f,ax=plt.subplots(nrows=1,ncols=2)
ac_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[0],figsize=(20,10))
grid_data.loc["2016-07-29 00:00:00":"2016-07-29 23:59:59"].plot.line(ax=ax[1],figsize=(20,10))
ax[0].set_title("AC Data")
ax[1].set_title("Grid Data")
Out[13]:
Text(0.5,1,'Grid Data')
<Figure size 432x288 with 0 Axes>
In [14]:
training_iters = 2000
learning_rate = 0.001
batch_size = 128
#both placeholders are of type float
x1 = tf.placeholder("float", [None, 1,1440,1])
y1 = tf.placeholder("int64", [None, 2])
In [15]:
train_X=ac_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]
rows=train_X.size/1440
print("rows = "+str(rows))
train_X=train_X.append(washer_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])
test_X=washer_data.loc["2016-07-20 00:00:00":"2016-11-05 23:59:59"]
test_X=test_X.append(ac_data.loc["2016-11-07 00:00:00":"2016-12-31 23:59:59"])
print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))
train_X=train_X.values.reshape(164,1,1440,1)
test_X=test_X.values.reshape(164,1,1440,1)
train_Y=pd.DataFrame([])
test_Y=pd.DataFrame([])
print ("after reshape train_X[0] shape= "+ str(train_X.shape[0]))
for i in range(train_X.shape[0]):
if i<rows:
train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0}))
else:
train_Y=train_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1}))
for i in range(train_X.shape[0]):
if i<rows:
test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":0,"other":1}))
else:
test_Y=test_Y.append(pd.DataFrame({"Sno":[i],"AC":1,"other":0}))
train_Y.set_index("Sno",inplace=True)
train_Y=train_Y.values.reshape(164,2)
test_Y.set_index("Sno",inplace=True)
test_Y=test_Y.values.reshape(164,2)
print("train_Y data = "+str(train_Y))
print ("train_Y shape= "+str(train_Y.shape))
print ("test_Y shape= "+str(test_Y.shape))
print ("train_X size= "+str(train_X.size))
print("train_X shape= "+str(train_X.shape))
rows = 109.0 train_X size= 236160 train_X shape= (236160,) after reshape train_X[0] shape= 164 train_Y data = [[1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [1 0] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1] [0 1]] train_Y shape= (164, 2) test_Y shape= (164, 2) train_X size= 236160 train_X shape= (164, 1, 1440, 1)
In [16]:
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
axes[int(i/2),0].plot(train_X[i,0])
axes[int(i/2),1].plot(train_X[i+1,0])
if (train_Y[i,0])==1:
axes[int(i/2),0].set_title("AC Data")
else:
axes[int(i/2),0].set_title("Washer Data")
if (train_Y[i+1,0])==1:
axes[int(i/2),1].set_title("AC Data")
else:
axes[int(i/2),1].set_title("Washer Data")
plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
In [17]:
plt.figure()
ndays=82
f,axes=plt.subplots(nrows=ndays,ncols=2,figsize=(20,200))
for i in range(0,ndays*2,2):
axes[int(i/2),0].plot(test_X[i,0])
axes[int(i/2),1].plot(test_X[i+1,0])
if (test_Y[i,0])==1:
axes[int(i/2),0].set_title("AC Data")
else:
axes[int(i/2),0].set_title("Washer Data")
if (test_Y[i+1,0])==1:
axes[int(i/2),1].set_title("AC Data")
else:
axes[int(i/2),1].set_title("Washer Data")
plt.subplots_adjust(bottom=0.1, right=0.8, top=4)
plt.show()
<Figure size 432x288 with 0 Axes>
In [18]:
def conv2d(x, W, b, strides=1):
# Conv2D wrapper, with bias and relu activation
x = tf.nn.conv2d(x, W, strides=[1, strides, strides, 1], padding='SAME')
x = tf.nn.bias_add(x, b)
return tf.nn.relu(x)
def maxpool2d(x, k=2):
return tf.nn.max_pool(x, ksize=[1, k, k, 1], strides=[1, k, k, 1],padding='SAME')
In [19]:
with tf.variable_scope("",reuse = tf.AUTO_REUSE):
weights = {
'wc1': tf.get_variable('W1', shape=(1,3,1,32), initializer=tf.contrib.layers.xavier_initializer()),
'wc2': tf.get_variable('W2', shape=(1,3,32,64), initializer=tf.contrib.layers.xavier_initializer()),
'wc3': tf.get_variable('W3', shape=(1,3,64,128), initializer=tf.contrib.layers.xavier_initializer()),
'wd1': tf.get_variable('W4', shape=(180*128,128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('W5', shape=(128,2), initializer=tf.contrib.layers.xavier_initializer()),
}
biases = {
'bc1': tf.get_variable('B1', shape=(32), initializer=tf.contrib.layers.xavier_initializer()),
'bc2': tf.get_variable('B2', shape=(64), initializer=tf.contrib.layers.xavier_initializer()),
'bc3': tf.get_variable('B3', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'bd1': tf.get_variable('B4', shape=(128), initializer=tf.contrib.layers.xavier_initializer()),
'out': tf.get_variable('B5', shape=(2), initializer=tf.contrib.layers.xavier_initializer()),
}
In [20]:
def conv_net(x, weights, biases):
# here we call the conv2d function we had defined above and pass the input image x, weights wc1 and bias bc1.
conv1 = conv2d(x, weights['wc1'], biases['bc1'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 14*14 matrix.
conv1 = maxpool2d(conv1, k=2)
# Convolution Layer
# here we call the conv2d function we had defined above and pass the input image x, weights wc2 and bias bc2.
conv2 = conv2d(conv1, weights['wc2'], biases['bc2'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 7*7 matrix.
conv2 = maxpool2d(conv2, k=2)
conv3 = conv2d(conv2, weights['wc3'], biases['bc3'])
# Max Pooling (down-sampling), this chooses the max value from a 2*2 matrix window and outputs a 4*4.
conv3 = maxpool2d(conv3, k=2)
# Fully connected layer
# Reshape conv2 output to fit fully connected layer input
fc1 = tf.reshape(conv3, [-1, weights['wd1'].get_shape().as_list()[0]])
fc1 = tf.add(tf.matmul(fc1, weights['wd1']), biases['bd1'])
fc1 = tf.nn.relu(fc1)
# Output, class prediction
# finally we multiply the fully connected layer with the weights and add a bias term.
out = tf.add(tf.matmul(fc1, weights['out']), biases['out'])
return out
In [21]:
pred = conv_net(x1, weights, biases)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y1))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
WARNING:tensorflow:From <ipython-input-21-0d1f03f2adbb>:3: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version. Instructions for updating: Future major versions of TensorFlow will allow gradients to flow into the labels input on backprop by default. See tf.nn.softmax_cross_entropy_with_logits_v2.
In [22]:
#Here you check whether the index of the maximum value of the predicted image is equal to the actual labelled image. and both will be a column vector.
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y1, 1))
#calculate accuracy across all the given images and average them out.
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
In [23]:
# Initializing the variables
init = tf.global_variables_initializer()
print(training_iters)
2000
In [24]:
with tf.Session() as sess:
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
saver = tf.train.Saver()
summary_writer = tf.summary.FileWriter('./Output', sess.graph)
for i in range(training_iters):
for batch in range(len(train_X)//batch_size):
batch_x = train_X[batch*batch_size:min((batch+1)*batch_size,len(train_X))]
batch_y = train_Y[batch*batch_size:min((batch+1)*batch_size,len(train_Y))]
'''print (batch_x.shape)
print (batch_x.dtype)
print (batch_y.shape)
print (batch_y.dtype)'''
batch_x.astype(float)
'''plt.figure()
plt.plot(batch_x[1,0])'''
# Run optimization op (backprop).
# Calculate batch loss and accuracy
opt = sess.run(optimizer, feed_dict={x1: batch_x,
y1: batch_y})
loss, acc = sess.run([cost, accuracy], feed_dict={x1: batch_x,
y1: batch_y})
print("Iter " + str(i) + ", Loss= " + \
"{:.6f}".format(loss) + ", Training Accuracy= " + \
"{:.5f}".format(acc))
print("Optimization Finished!")
# Calculate accuracy for all 10000 mnist test images
test_acc,valid_loss = sess.run([accuracy,cost], feed_dict={x1: test_X, y1: test_Y})
train_loss.append(loss)
test_loss.append(valid_loss)
train_accuracy.append(acc)
test_accuracy.append(test_acc)
print("Testing Accuracy:","{:.5f}".format(test_acc))
save_path = saver.save(sess, "/tmp/model.ckpt",global_step=i,max_to_keep=500,keep_checkpoint_every_n_hours=0.1,)
//save_path = saver.save(sess, "/tmp/model"+str(i)+".ckpt",max_to_keep=500,keep_checkpoint_every_n_hours=0.5,)
print ("data saved in","/tmp/model"+str(i)+".ckpt")
summary_writer.close()
Iter 0, Loss= 1.400497, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model0.ckpt Iter 1, Loss= 1.461437, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model1.ckpt Iter 2, Loss= 1.208775, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model2.ckpt Iter 3, Loss= 0.941657, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model3.ckpt Iter 4, Loss= 0.698538, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model4.ckpt Iter 5, Loss= 0.482386, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model5.ckpt Iter 6, Loss= 0.352138, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model6.ckpt Iter 7, Loss= 0.333581, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model7.ckpt Iter 8, Loss= 0.388094, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model8.ckpt Iter 9, Loss= 0.421045, Training Accuracy= 0.59375 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model9.ckpt Iter 10, Loss= 0.405410, Training Accuracy= 0.59375 Optimization Finished! Testing Accuracy: 0.66463 data saved in /tmp/model10.ckpt Iter 11, Loss= 0.362444, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model11.ckpt Iter 12, Loss= 0.327008, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model12.ckpt Iter 13, Loss= 0.328009, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model13.ckpt Iter 14, Loss= 0.353359, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model14.ckpt Iter 15, Loss= 0.363980, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model15.ckpt Iter 16, Loss= 0.351866, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model16.ckpt Iter 17, Loss= 0.332383, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model17.ckpt Iter 18, Loss= 0.322081, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model18.ckpt Iter 19, Loss= 0.326000, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model19.ckpt Iter 20, Loss= 0.335164, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model20.ckpt Iter 21, Loss= 0.339735, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model21.ckpt Iter 22, Loss= 0.336444, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model22.ckpt Iter 23, Loss= 0.328512, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model23.ckpt Iter 24, Loss= 0.322519, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model24.ckpt Iter 25, Loss= 0.322648, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model25.ckpt Iter 26, Loss= 0.327237, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model26.ckpt Iter 27, Loss= 0.330693, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model27.ckpt Iter 28, Loss= 0.329727, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model28.ckpt Iter 29, Loss= 0.325636, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model29.ckpt Iter 30, Loss= 0.322174, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model30.ckpt Iter 31, Loss= 0.321787, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model31.ckpt Iter 32, Loss= 0.324042, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model32.ckpt Iter 33, Loss= 0.325073, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model33.ckpt Iter 34, Loss= 0.323922, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model34.ckpt Iter 35, Loss= 0.321958, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model35.ckpt Iter 36, Loss= 0.321601, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model36.ckpt Iter 37, Loss= 0.323007, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model37.ckpt Iter 38, Loss= 0.323229, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model38.ckpt Iter 39, Loss= 0.322024, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model39.ckpt Iter 40, Loss= 0.321299, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model40.ckpt Iter 41, Loss= 0.321710, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model41.ckpt Iter 42, Loss= 0.322303, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model42.ckpt Iter 43, Loss= 0.322050, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model43.ckpt Iter 44, Loss= 0.321194, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model44.ckpt Iter 45, Loss= 0.321051, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model45.ckpt Iter 46, Loss= 0.321480, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model46.ckpt Iter 47, Loss= 0.321118, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model47.ckpt Iter 48, Loss= 0.320579, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model48.ckpt Iter 49, Loss= 0.320925, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model49.ckpt Iter 50, Loss= 0.320724, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model50.ckpt Iter 51, Loss= 0.320502, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model51.ckpt Iter 52, Loss= 0.320246, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model52.ckpt Iter 53, Loss= 0.319962, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model53.ckpt Iter 54, Loss= 0.319671, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model54.ckpt Iter 55, Loss= 0.319412, Training Accuracy= 0.85156 Optimization Finished! Testing Accuracy: 0.33537 data saved in /tmp/model55.ckpt Iter 56, Loss= 0.319568, Training Accuracy= 0.85156 Optimization Finished! 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Testing Accuracy: 1.00000 data saved in /tmp/model1995.ckpt Iter 1996, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1996.ckpt Iter 1997, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1997.ckpt Iter 1998, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1998.ckpt Iter 1999, Loss= 0.000010, Training Accuracy= 1.00000 Optimization Finished! Testing Accuracy: 1.00000 data saved in /tmp/model1999.ckpt
In [45]:
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_loss, 'b', label='Training loss')
plt.plot(range(len(train_loss)), test_loss, 'r', label='Test loss')
plt.title('Training and Test loss')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.legend()
plt.show()
In [46]:
plt.figure(figsize=(10,10))
plt.plot(range(len(train_loss)), train_accuracy, 'b', label='Training Accuracy')
plt.plot(range(len(train_loss)), test_accuracy, 'r', label='Test Accuracy')
plt.title('Training and Test Accuracy')
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Acurracy',fontsize=16)
plt.legend()
plt.show()
In [39]:
saver = tf.train.Saver()
with tf.variable_scope("", reuse = True):
with tf.Session() as sess:
saver.restore(sess, "/tmp/model1998.ckpt")
print("Model restored.")
#sess.run(init)
#print (tf.variable_scope.get_variable_scope())
w=tf.get_variable("W1")
w=w.read_value().eval()
#print(w.read_value().eval())
plt.subplot(421)
plt.subplots_adjust(bottom=0.1, right=4, top=4)
curr_img = np.reshape(w, (3,32))
plt.title ("iter 1 W1")
plt.imshow(curr_img, cmap='gray')
w1=tf.get_variable("W2")
w1=w1.read_value().eval()
plt.subplot(423)
curr_img = np.reshape(w1, (64,96))
plt.title ("iter 1 W2")
plt.imshow(curr_img, cmap='gray')
saver.restore(sess, "/tmp/model1999.ckpt")
print("Model restored.")
w1=tf.get_variable("W1")
w1=w1.read_value().eval()
plt.subplot(422)
curr_img = np.reshape(w1, (3,32))
plt.title ("iter 2 W1")
plt.imshow(curr_img, cmap='gray')
w1=tf.get_variable("W2")
w1=w1.read_value().eval()
plt.subplot(424)
curr_img = np.reshape(w1, (64,96))
plt.title ("iter 2 W2")
plt.imshow(curr_img, cmap='gray')
cax = plt.axes([0.1, 0.5, 0.75, 0.8])
plt.colorbar(cax=cax)
INFO:tensorflow:Restoring parameters from /tmp/model1998.ckpt Model restored. INFO:tensorflow:Restoring parameters from /tmp/model1999.ckpt Model restored.
